A Framework for Scalable Cooperative Navigation of Autonomous Vehicles

We describe a general framework for controlling and coordinating a group of non-holonomic mobile robots equipped with range sensors, with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. We first describe a set of control laws that allows each robot to control its position and orientation with respect to neighboring robots or obstacles in the environment. We then develop a coordination protocol that allows the robots to automatically switch between the control laws to follow a specified trajectory. Finally, we describe two simple trajectory generators that are derived from potential field theory. The first allows each robot to plan its reference trajectory based on the information available to it. The second scheme requires sharing of information and results in a trajectory for the designated leader. Numerical simulations illustrate the application of these ideas and demonstrate the scalability of the proposed framework for a large group of robots

Vision-based control of multi-agent systems

Scope and Methodology of Study: Creating systems with multiple autonomous vehicles places severe demands on the design of decision-making supervisors, cooperative control schemes, and communication strategies. In last years, several approaches have been developed in the literature. Most of them solve the vehicle coordination problem assuming some kind of communications between team members. However, communications make the group sensitive to failure and restrict the applicability of the controllers to teams of friendly robots. This dissertation deals with the problem of designing decentralized controllers that use just local sensor information to achieve some group goals.Findings and Conclusions: This dissertation presents a decentralized architecture for vision-based stabilization of unmanned vehicles moving in formation. The architecture consists of two main components: (i) a vision system, and (ii) vision-based control algorithms. The vision system is capable of recognizing and localizing robots. It is a model-based scheme composed of three main components: image acquisition and processing, robot identification, and pose estimation.Using vision information, we address the problem of stabilizing groups of mobile robots in leader- or two leader-follower formations. The strategies use relative pose between a robot and its designated leader or leaders to achieve formation objectives. Several leader-follower formation control algorithms, which ensure asymptotic coordinated motion, are described and compared. Lyapunov's stability theory-based analysis and numerical simulations in a realistic tridimensional environment show the stability properties of the control approaches

A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. Our software framework provides the methodology and the tools that enable robots to exhibit deliberative and reactive behaviors in autonomous operation, to be reprogrammed by a human operator at run-time, and to learn and adapt to unstructured, dynamic environments and new tasks, while providing performance guarantees. We demonstrate the algorithms and software on an experimental testbed that involves a team of car-like robots using a single omnidirectional camera as a sensor without explicit use of odometry

Coordinated multi-robot formation control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Distributed selection of flight formation in UAV missions

Recent advances in sensor, processor and airframe technologies allow coordination of large groups of autonomous unmanned aerial vehicles (UAV) today. Reconfiguration of the formation is sometimes necessary in order to accomplish a mission’s objectives. A centralised solution to optimal reconfiguration may often be either impossible or intractable due to sensor, communication, physical, computational restrictions. Thus a distributed approach may be more appropriate to accommodate real-world scenarios. In this article we propose a novel distributed control method, which is divided into two modules: a leaderfollower module, which allows UAVs to keep a pre-specified formation, and a decision making module that allows UAVs to choose among various available formations in an optimum sense. UAVs choose the best formation to accomplish each part of the mission and retain this formation till the next way-point. The simulation presented uses a 5-leg mission and Parrot AR-drones are used as test-beds to demonstrate the usefulness of the proposed distributed controller

Leader-Follower Control and Distributed Communication based UAV Swarm Navigation in GPS-Denied Environment

Unmanned Aerial Vehicles (UAVs) have developed rapidly in recent years due to technological advances and UAV technology finds applications in a wide range of fields, including surveillance, search and rescue, and agriculture. The utilization of UAV swarms in these contexts offers numerous advantages, increasing their value across different industries. These advantages include increased efficiency in tasks, enhanced productivity, greater safety, and the higher data quality. The coordination of UAVs becomes particularly crucial during missions in these applications, especially when drones are flying in close proximity as part of a swarm. For instance, if a drone swarm is targeted or needs to navigate through a Global Positioning System (GPS)-denied environment, it may encounter challenges in obtaining the location information typically provided by GPS. This poses a new challenge for the UAV swarms to maintain a reliable formation and successfully complete a given mission. In this article, our objective is to minimize the number of sensors required on each UAV and reduce the amount of information exchanged between UAVs. This approach aims to ensure the reliable maintenance of UAV formations with minimal communication requirements among UAVs while they follow predetermined trajectories during swarm missions. In this paper, we introduce a concept that utilizes extended Kalman filter, leader-follower-based control and a distributed data-sharing scheme to ensure the reliable and safe maintenance of formations and navigation autonomously for UAV swarm missions in GPS-denied environments. The formation control approaches and control strategies for UAV swarms are also discussed

Formation Navigation and Relative Localisation of Multi-Robot Systems

When proceeding from single to multiple robots, cooperative action is one of the most relevant topics. The domain of robotic security systems contains typical applications for a multi-robot system (MRS). Possible scenarios are safety and security issues on airports, harbours, large industry plants or museums. Additionally, the field of environmental supervision is an up-coming issue. Inherent to these applications is the need for an organised and coordinated navigation of the robots, and a vital prerequisite for any coordinated movements is a good localisation. This dissertation will present novel approaches to the problems of formation navigation and relative localisation with multiple ground-based mobile robots. It also looks into the question what kind of metric is applicable for multi-robot navigation problems. Thereby, the focus of this work will be on aspects of 1. coordinated navigation and movement A new potential-field-based approach to formation navigation is presented. In contradiction to classical potential-field-based formation approaches, the proposed method also uses the orientation between neighbours in the formation. Consequently, each robot has a designated position within the formation. Therefore, the new method is called directed potential field approach. Extensive experiments prove that the method is capable of generating all kinds of formation shapes, even in the presence of dense obstacles. All tests have been conducted with simulated and real robots and successfully guided the robot formation through environments with varying obstacle configurations. In comparison, the nondirected potential field approach turns out to be unstable regarding the positions of the robots within formations. The robots strive to switch their positions, e.g. when passing through narrow passages. Under such conditions the directed approach shows a preferable behaviour, called “breathing”. The formation shrinks or inflates depending on the obstacle situation while trying to maintain its shape and keep the robots at their desired positions inside the formation. For a more particular comparison of formation algorithms it is important to have measures that allow a meaningful evaluation of the experimental data. For this purpose a new formation metric is developed. If there are many obstacles, the formation error must be scaled down to be comparable to an empty environment where the error would be small. Assuming that the environment is unknown and possibly non-static, only actual sensor information can be used for these calculations. We developed a special weighting factor, which is inverse proportional to the “density” of obstacles and which turns out to model the influence of the environment adequately. 2. relative localisation A new method for relative localisation between the members of a robot group is introduced. This relative localisation approach uses mutual sensor observations to localise the robots with respect to other objects – without having an environment model. Techniques like the Extended Kalman Filter (EKF) have proven to be powerful tools in the field of single robot applications. This work presents extensions to these algorithms with respect to the use in MRS. These aspects are investigated and combined under the topic of improving and stabilising the performance of the localisation and navigation process. Most of the common localisation approaches use maps and/or landmarks with the intention of generating a globally consistent world-coordinate system for the robot group. The aim of the here presented relative localisation approach, on the other hand, is to maintain only relative positioning between the robots. The presented method enables a group of mobile robots to start at an unknown location in an unknown environment and then to incrementally estimate their own positions and the relative locations of the other robots using only sensor information. The result is a robust, fast and precise approach, which does not need any preconditions or special assumptions about the environment. To validate the approach extensive tests with both, real and simulated, robots have been conducted. For a more specific evaluation, the Mean Localisation Error (MLE) is introduced. The conducted experiments include a comparison between the proposed Extended Kalman Filter and a standard SLAM-based approach. The developed method robustly delivered an accuracy better than 2 cm and performed at least as well as the SLAM approach. The algorithm coped with scattered groups of robots while moving on arbitrarily shaped paths. In summary, this thesis presents novel approaches to the field of coordinated navigation in multi-robot systems. The results facilitate cooperative movements of robot groups as well as relative localisation among the group members. In addition, a solid foundation for a non-environment related metric for formation navigation is introduced